F. Quiller e and S. Rajopadhye. Optimizing memory usage in the polyhedral model. In ACM Transactions on Programming Languages and Systems (TOPLAS),Volume 22, Issue 5, pages 773--815, Sept. 2000. 4.1.4  Home/Search   Document Not in Database   Summary   Related Articles   Check

....power and size [20, 23] So further effort is required here. exploiting the limited life times of the data in the program to overlap them in an as tight as possible data layout to reduce both the main memory size and the capacity misses in the L2 or L1 cache. An advanced in place mapping approach [8, 15, 22] enables a better exploitation of the caches and it allows to remove nearly all capacity misses. The results are very promising both for software controlled caches [11] as in the Philips TriMedia processor, and for more traditional hardware controlled ones like the Pentium [12] At the same time, ....

F.Quillere, S.Rajopadhye, "Optimizing memory usage in the polyhedral model", Proc. Massively Parallel Computer Systems Conf., April 1998.

....on a parameterized version of the input program, avoiding the need to expand a graph for varying parameters and problem sizes, and it can often reduce to a linear program for flexible and e#cient optimization. Polyhedral representations have also been utilized for powerful storage optimizations [22, 28, 31, 20]. In this paper, we aim to bridge the gap and employ the polyhedral representations of the scientific community to analyze the synchronous dataflow graphs of the DSP community. Towards this end, we present a translation from a dataflow graph to a System of A#ne Recurrence Equations (SARE) which ....

....optimization problems that are already the focus of the DSP community. The polyhedral model is appealing because it provides a linear algebra framework that is simple, flexible, and e#cient. 7.3. 1 Bu#er Minimization Storage optimization is one area in which both the scientific community [22, 28, 31, 20] and the DSP community [25, 12, 24] have invested a great deal of energy. Both communities have invented schemes for detecting live ranges, collapsing arrays across dead locations, and sharing bu#ers arrays between di#erent 14 statements. It will be an interesting avenue for future work to use ....

F. Quillere and S. Rajopadhye. Optimizing memory usage in the polyhedral model. ACM Transactions on Programming Languages and Systems, 22(5):773--815, September 2000.

....on a parameterized version of the input program, avoiding the need to expand a graph for varying parameters and problem sizes, and it can often reduce to a linear program for exible and ecient optimization. Polyhedral representations have also been utilized for powerful storage optimizations [22, 28, 31, 20]. In this paper, we aim to bridge the gap and employ the polyhedral representations of the scienti c community to analyze the synchronous data ow graphs of the DSP community. Towards this end, we present a translation from a data ow graph to a System of Ane Recurrence Equations (SARE) which are ....

....optimization problems that are already the focus of the DSP community. The polyhedral model is appealing because it provides a linear algebra framework that is simple, exible, and ecient. 7.3. 1 Bu er Minimization Storage optimization is one area in which both the scienti c community [22, 28, 31, 20] and the DSP community [25, 12, 24] have invested a great deal of energy. Both communities have invented schemes for detecting live ranges, collapsing arrays across dead locations, and sharing bu ers arrays between di erent 14 statements. It will be an interesting avenue for future work to use ....

F. Quillere and S. Rajopadhye. Optimizing memory usage in the polyhedral model. ACM Transactions on Programming Languages and Systems, 22(5):773-815, September 2000.

....3 Geometrical Program Modeling The novelty of our approach lies in the geometric model on which our technique is based. Geometrical models have in the past been quite extensively used to model and analyse execution of program statements, in the parallel compiler and regular array synthesis domain [9,17,18]. Though the model itself is quite simple, it concisely represents most of the necessary information about the data and control flow in the program. We use formulas that encode a#ne constraints on integer variables, logical connectives and quantifiers, also called Presburger formulas, to ....

....They are equivalent if they are weakly equivalent and for all 1 m: u i = v i . The buf[ variable has the same number of elements as A[ this is because of the requirement of single assignment form. Later transformation steps in the full transformation script remove these redundancies [5,18]. i: v[ f(u 1 ,u 2 , u n ) j: v[ g(u 1 ,u 2 , u m ) k: v[ f(u 1 ,u 2 , u n ) h : buf[ u 2 [ i : v[ f(u 1 ,buf, u n ) j : v[ g(u 1 ,buf, u m ) k : v[ f(u 1 ,u 2 , u n ) l : v[ f(u 1 ....

Quillere, F., and S. Rajopadhye, Optimizing memory usage in the polyhedral model. ACM TOPLAS, 22(5):773-815. 2000.

....across statements and loop nests. Moreover, our framework goes beyond AOV s to unify the notion of occupancy vectors with known ane scheduling techniques. The leading method for schedule speci c storage optimization in the context of the polyhedral model is that of Quiller e and Rajopadhye [21], which builds on that of Wilde and Rajopadhye [25] Like our technique, their analysis targets static control ow programs in a single assignment form. However, they also support multidimensional ane schedules, as well as multiple dimensions of storage reuse. The technique utilizes projective ....

F. Quillere and S. Rajopadhye. Optimizing memory usage in the polyhedral model. ACM Transactions on Programming Languages and Systems, 22(5):773{ 815, September 2000.

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F. Quiller# et S. Rajopadhye. Optimizing memory usage in the polyhedral model. Transactions on Programming Languages and Systems, vol. 22, n 5, sep 2000, pp. 773815.

No context found.

F. Quiller e and S. Rajopadhye. Optimizing memory usage in the polyhedral model. In ACM Transactions on Programming Languages and Systems (TOPLAS),Volume 22, Issue 5, pages 773--815, Sept. 2000. 4.1.4

No context found.

F. Quiller e and S. Rajopadhye. Optimizing memory usage in the polyhedral model. In ACM Transactions on Programming Languages and Systems (TOPLAS),Volume 22, Issue 5, pages 773--815, Sept. 2000. 4.1.4

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